Advances and pitfalls in the analysis and interpretation of resting-state FMRI data

Cole Cole

doi:10.3389/fnsys.2010.00008

Abstract

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.

Highlights

Spontaneous,or‘resting-state’,fluctuations in the blood oxygenation level-dependent (BOLD) signal, as measured by functional magnetic resonance imaging (FMRI), may present a valuable data resource for delineating the human neural functional architecture
Consistent, large-scale spatial patterns of coherent signal have been identified in the human brain using both FMRI (Biswal et al, 1995; Lowe et al, 1998) and positron emission tomography (PET; Shulman et al, 1997; Raichle et al, 2001)
The majority of approaches to analysing resting-state FMRI data have far been spatially model-driven, with strong a priori hypotheses regarding the functional connectivity of a small number of brain regions of interest (ROIs) or individual voxel locations of interest

Summary

SYSTEMS NEUROSCIENCE

Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Reviewed by: Thomas Meindl, University of Munich, Germany Catie Chang, Stanford University, USA. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences.The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved.

INTRODUCTION

Analysis and interpretation of RSNs

OF RSN IDENTIFICATION